01
AI assistants and knowledge bases
Answers grounded in internal documents, procedures, products or regulatory sources with retrieval and source references.
RAGEmbeddingsVector search
AI / Automation
AI automation for business.
With sources, boundaries and control.
AI workflow automation, assistants, RAG, document processing, CRM, Telegram and API integrations. I use AI where it removes real manual work, with data, validation and a clear fallback path.
If AI is not the right tool, I will say so before development.
RAG / KnowledgeOCR / DocumentsAI assistantsCRM / TelegramAPI / Webhooks
Business cases
Where AI actually
creates value.
I do not start with a model. I start with where the workflow loses time, information or decision quality, and only then design the AI layer.
02
Document processing
OCR, fact extraction, classification, normalization, contradiction detection and structured data from documents.
OCRExtractionClassification
03
AI inside a product
Context-aware assistants, draft generation, analysis, recommendations and automated flows inside SaaS or MVP products.
LLMContextProduct AI
04
Workflow automation
CRM, Telegram, external APIs, webhooks, queues, notifications and rules that remove manual hops between systems.
CRMTelegramQueues
Reliability first
The value is not just the model answer.
It is knowing when to trust it.
Business workflows need to show where an answer came from, what happens when evidence is missing and how the system behaves when an external service fails.
Source grounding
Answers and extracted facts can be linked to the document, fragment or record that supports them.
Explicit boundaries
The system should be able to say “not enough evidence” instead of turning fluent language into false certainty.
Predictable states
Timeouts, retries, duplicate events and unavailable APIs are normal production states, not edge cases to ignore.
Human fallback
Critical decisions can be routed to review instead of triggering an automatic action when confidence is low.
History and auditability
Important workflows preserve context: what arrived, what was processed, what result was produced and what happened next.
Access and data
Roles, private documents, API keys and sensitive information are part of the architecture from the start.
AI fit check
Sometimes the best AI project is the one you do not build.
If a reliable rule, search feature or integration solves the task more cheaply, adding an LLM for fashion is a mistake. AI earns its place when the context varies, data is unstructured or manual processing is too expensive.
- +There is a lot of unstructured knowledge or documents
- +People spend time searching, reading and comparing
- +The task requires natural language and context
- +Errors can be detected, bounded or routed to review
- —The rule can be expressed with a few deterministic conditions
- —The result must be fully deterministic
- —There is no reliable data for the system to ground on
- —The cost of an unchecked error is higher than the automation value
How I build it
AI is one layer.
The product around it matters more.
Workflow discovery
Define the user, inputs, target decision and acceptable cost of error.
Data / RAG design
Design sources, storage, chunking, metadata, embeddings, refresh rules and retrieval.
Prototype on real data
Test the approach on real examples before building a large UI and infrastructure around an unproven idea.
Production workflow
Add backend, queues, access rules, retries, history, monitoring, human fallback and the real user flow.
Evaluation & iteration
Define quality criteria and improve retrieval, prompts and model choice using measured errors rather than a handful of impressive demos.
No black box
The client should understand what they are buying.
I do not sell “a neural network”. I show what data is used, where AI sits, which parts remain normal software logic and what happens when the model or an external API does not respond.
- +Architecture is explained in plain language
- +Code and key access stay with the client
- +A narrow pilot on real data can come first
- +I do not add an LLM where a deterministic rule is better
Trust / How I work
Less uncertainty.
More control.
The stressful part of software is rarely just the price. It is not knowing what is happening, who owns the outcome, or what remains under your control after launch. I design the process to make those things visible from the start.
01
You always know what is happening
The project has clear stages, a current state and a next action. No vague “almost done” status and no technical fog.
02
Source code and access stay with you
The codebase, structure and key credentials do not become leverage. The product can be continued without artificial vendor lock-in.
03
You talk directly to the developer
No manager translating between the problem and the implementation. Context stays intact and decisions can be discussed directly.
04
Risk before code
Before active development, I review scope, integrations, data, critical flows and launch constraints. That is cheaper than repairing the architecture after release.
Verify before reaching outFirst request — no registration required
FAQ / AI projects
Questions worth answering before we start.
01Can you build a chat over my documents?+
Yes, but the result depends on more than the model. We need to understand document types, volume, text quality, access rules, source freshness and how the user verifies answers.
02Which model will be used?+
Model choice comes after requirements for quality, speed, cost and privacy. The architecture should not be locked to one provider without a reason.
03Can AI answer without errors?+
No LLM can guarantee zero errors. Serious workflows need retrieval, sources, validation, boundaries, confidence handling and human review where the cost of error is high.
04Can it process PDFs, scans and photos?+
Yes. That needs a separate ingestion/OCR pipeline: recognition, normalization, document structure, metadata and quality control before content reaches the LLM.
05Can we start with an MVP?+
Yes. Usually that is the best path: pick one valuable scenario, test it on real data, measure quality and expand only after it works.
Start with the problem
Do not tell me about AI first. Tell me what people are doing manually today.
From that, we can decide whether the right solution is RAG, OCR, an LLM, a classic integration, or a combination.
First request without mandatory registration · you can attach sample documents